Lecture8 - From CBR to IBk

1) Instance based learning and case-based reasoning (CBR) provide frameworks for incorporating learning into k-nearest neighbors (kNN) classification. 2) CBR formalizes kNN into five phases: preprocessing training data, retrieving similar cases, reusing solutions, revising solutions if needed, and retaining lessons. 3) Key challenges for CBR include reducing the cost of case matching, automatically generating distance functions tailored to problems, and extracting explanations from cases.